Merge branch 'main' into fix-2814-remove-backticks

Author: svekars

advanced_source/dynamic_quantization_tutorial.py

@@ -134,10 +134,18 @@ def tokenize(self, path):
 # -----------------------------
 #
 # This is a tutorial on dynamic quantization, a quantization technique
-# that is applied after a model has been trained. Therefore, we'll simply load some
-# pretrained weights into this model architecture; these weights were obtained
-# by training for five epochs using the default settings in the word language model
-# example.
+# that is applied after a model has been trained. Therefore, we'll simply
+# load some pretrained weights into this model architecture; these
+# weights were obtained by training for five epochs using the default
+# settings in the word language model example.
+#
+# Before running this tutorial, download the required pre-trained model:
+#
+# .. code-block:: bash
+#
+#     wget https://s3.amazonaws.com/pytorch-tutorial-assets/word_language_model_quantize.pth
+#
+# Place the downloaded file in the data directory or update the model_data_filepath accordingly.
 
 ntokens = len(corpus.dictionary)
 

beginner_source/examples_autograd/polynomial_custom_function.py

@@ -33,8 +33,11 @@ def forward(ctx, input):
         """
         In the forward pass we receive a Tensor containing the input and return
         a Tensor containing the output. ctx is a context object that can be used
-        to stash information for backward computation. You can cache arbitrary
-        objects for use in the backward pass using the ctx.save_for_backward method.
+        to stash information for backward computation. You can cache tensors for
+        use in the backward pass using the ``ctx.save_for_backward`` method. Other
+        objects can be stored directly as attributes on the ctx object, such as
+        ``ctx.my_object = my_object``. Check out `Extending torch.autograd <https://docs.pytorch.org/docs/stable/notes/extending.html#extending-torch-autograd>`_
+        for further details.
         """
         ctx.save_for_backward(input)
         return 0.5 * (5 * input ** 3 - 3 * input)